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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from itertools import product
import torch
from fvcore.common.benchmark import benchmark
from tests.test_packed_to_padded import TestPackedToPadded
def bm_packed_to_padded() -> None:
kwargs_list = []
backend = ["cpu"]
if torch.cuda.is_available():
backend.append("cuda:0")
num_meshes = [2, 10, 32]
num_verts = [100, 1000]
num_faces = [300, 3000]
num_ds = [0, 1, 16]
test_cases = product(num_meshes, num_verts, num_faces, num_ds, backend)
for case in test_cases:
n, v, f, d, b = case
kwargs_list.append(
{"num_meshes": n, "num_verts": v, "num_faces": f, "num_d": d, "device": b}
)
benchmark(
TestPackedToPadded.packed_to_padded_with_init,
"PACKED_TO_PADDED",
kwargs_list,
warmup_iters=1,
)
benchmark(
TestPackedToPadded.packed_to_padded_with_init_torch,
"PACKED_TO_PADDED_TORCH",
kwargs_list,
warmup_iters=1,
)
if __name__ == "__main__":
bm_packed_to_padded()
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